Dynamic Graphs Analysis of EEG

Abstract

In this study, we investigate the use of temporal dynamics in brain connectivity for the classification of electroencephalography (EEG) signals using dynamic Graph Neural Networks (GNNs). Our methods are applied to several large-scale EEG datasets focused on abnormality and epilepsy detection. The implemented models demonstrate competitive performance on unseen test subjects across all three datasets, outperforming previous graph-based baselines in terms of accuracy and F1 score. We explore multiple architectures designed to capture temporal variations in graph-structured data, demonstrating their effectiveness in modeling dynamic brain activity. In addition to classification, we employ graph-theoretical metrics to analyze temporal changes in brain networks, such as network efficiency and node degree, across time windows of EEG recordings. The goal is to characterize differences between pathological and healthy groups at both the node and network levels. We particularly examine epilepsy and healthy subject groups to highlight differences in local network efficiency and node degrees, with statistical significance confirmed via F-tests.